UTILIZING GPT 3.5 FOR ARABIC INTENT CLASSIFICATION WITH PROMPTING

Intent classification in natural language processing (NLP) is crucial for identifying user intents from their utterances. This task is particularly challenging for Arabic due to its complex morphology and high ambiguity. Recent advancements in NLP, including deep learning and transfer learning, have improved Arabic intent classification. However, a significant gap still exists compared to major languages like English. This paper proposes a new Arabic intent classification based on 1-shot and 3-shot techniques for investigating prompting strategies for Arabic in specific .The ’Temperature’ parameter is employed to modulate probability distributions and guide the model in classifying Arabic sentences into predefined categories. Results show that prompting, especially contrastive with dynamic reasoning, outperforms fine-tuning in both accuracy and resource efficiency, high- lighting the effectiveness of prompting techniques in enhancing Arabic NLP applications. Contrastive with Dynamic reasoning reported accuracy of 0.98% for 1- shot and 100% for 3-shots while fine tuning recorded 0.87% for training of the used Arabic intent dataset.